Cameron, Lorna | NHS Grampian, Aberdeen, United Kingdom

Scientific presentation (RPS 1205) at ECR 2024, 28.2.-2.3.2024 in Vienna, Austria.

Purpose:

The primary aim of this study was to evaluate whether an AI product with the ability to identify chest x-ray (CXR) images of highest risk of lung cancer can reduce the time between imaging and treatment in those patients subsequently diagnosed with lung cancer.

Method:

The NHS Grampian Innovation, Radiology and Cancer Teams collaborated with the Centre for Sustainable Delivery and the Scottish Health Technology Group to design an evaluation of the real-world impact of using an AI product designed to risk stratify CXR images. Full pathway mapping was carried out and baseline time delays between all key points (CXR, CXR reporting, CT, CT reporting, MDT diagnosis and treatment) were established. CXR images flagged as highest risk of lung cancer were expedited for CXR reporting, CT and CT reporting. NHS Grampian radiologists collaborated with the company to calibrate the product in ways that maximised identification of lung cancer whilst not overwhelming CT capacity.

Results:

Several months into the project the time between CXR and CT report has dropped from 22 to 10.3 days (N=132). Radiologists identified 28 images not flagged by the product about which they were concerned about cancer. Thus far, none of these patients have been diagnosed with cancer. Under the current calibration conditions, using radiologists’ judgements, the product performs at 84.4 sensitivity and 90.5 specificity (N=24071).

Conclusion:

Early results suggest AI risk stratification of CXR images may help healthcare organisations reduce the time taken to treat people diagnosed with lung cancer. This could be especially important for people who are diagnosed following CXR imaging for non-cancer reasons. In our region, this is about two thirds of people diagnosed with lung cancer.

 

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